Prediction of metabolite identity from accurate mass, migration time prediction and isotopic pattern information in CE-TOFMS data

Masahiro Sugimoto, Akiyoshi Hirayama, Martin Robert, Shinobu Abe, Tomoyoshi Soga, Masaru Tomita

Research output: Contribution to journalArticle

57 Citations (Scopus)


CE-TOFMS is a powerful method for profiling charged metabolites. However, the limited availability of metabolite standards hinders the process of identifying compounds from detected features in CE-TOFMS data sets. To overcome this problem, we developed a method to identify unknown peaks based on the predicted migration time (tm) and accurate m/z values. We developed a predictive model using 375 standard cationic metabolites and support vector regression. The model yielded good correlations between the predicted and measured tm (R=0.952 and 0.905 using complete and cross-validation data sets, respectively). Using the trained model, we subsequently predicted the tm for 2938 metabolites available from the public databases and assigned tentative identities to noise-filtered features in human urine samples. While 38.9% of the peaks were assigned metabolite names by matching with the standard library alone, the proportion increased to 52.2%. The proposed methodology increases the value of metabolomic data sets obtained from CE-TOFMS profiling.

Original languageEnglish
Pages (from-to)2311-2318
Number of pages8
Issue number14
Publication statusPublished - 2010 Jul 1



  • Metabolite identification
  • Metabolome
  • Non-target analysis

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Clinical Biochemistry

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